Artefact Removal from EEG Signals using Total Variation De-noising
Padmesh Tripathi1, Yogesh Kumar2, Vishwa Nath Jha3
1Dr Padmesh Tripathi*, Ph.D. Degree from Sharda University, Greater Noida, India.
2Dr. Yogesh Kumar, Associate Professor, Department of Applied Science and Humanities, IIMT College of Engineering , Greater Noida, India.
3Dr.Vishwa Nath Jha, Associate Professor, Department of Mathematics, Prince Sattam Bin Abdulaziz University, Wadi Adawaser, Saudi Arabia.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 24, 2020. | Manuscript published on March 10, 2020. | PP: 2357-2361 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2703039520/2020©BEIESP | DOI: 10.35940/ijitee.E2703.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Artefacts removing (de-noising) from EEG signals has been an important aspect for medical practitioners for diagnosis of health issues related to brain. Several methods have been used in last few decades. Wavelet and total variation based de-noising have attracted the attention of engineers and scientists due to their de-noising efficiency. In this article, EEG signals have been de-noised using total variation based method and results obtained have been compared with the results obtained from the celebrated wavelet based methods . The performance of methods is measured using two parameters: signal-to-noise ratio and root mean square error. It has been observed that total variation based de-noising methods produce better results than the wavelet based methods.
Keywords: Artefacts, EEG, Optimization, Regularizers, Total Variation De-Noising, Wavelets.
Scope of the Article: Cross-layer optimization